Overview

Brought to you by YData

Dataset statistics

Number of variables17
Number of observations473
Missing cells1215
Missing cells (%)15.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory227.3 KiB
Average record size in memory492.1 B

Variable types

Categorical6
Numeric10
Unsupported1

Alerts

ESTADO has constant value "SONORA"Constant
CLAVE SITIO is highly overall correlated with CUERPO DE AGUA and 5 other fieldsHigh correlation
COLI_FEC is highly overall correlated with E_COLIHigh correlation
CUERPO DE AGUA is highly overall correlated with CLAVE SITIO and 4 other fieldsHigh correlation
DBO_TOT is highly overall correlated with DQO_TOTHigh correlation
DQO_TOT is highly overall correlated with DBO_TOT and 2 other fieldsHigh correlation
E_COLI is highly overall correlated with COLI_FEC and 3 other fieldsHigh correlation
LATITUD is highly overall correlated with CLAVE SITIO and 2 other fieldsHigh correlation
LONGITUD is highly overall correlated with CLAVE SITIO and 4 other fieldsHigh correlation
MUNICIPIO is highly overall correlated with CLAVE SITIO and 2 other fieldsHigh correlation
N_TOT is highly overall correlated with E_COLI and 2 other fieldsHigh correlation
P_TOT is highly overall correlated with DQO_TOT and 2 other fieldsHigh correlation
SUBTIPO CUERPO AGUA is highly overall correlated with CLAVE SITIO and 4 other fieldsHigh correlation
TIPO CUERPO DE AGUA is highly overall correlated with CLAVE SITIO and 3 other fieldsHigh correlation
OD_mg/L has 114 (24.1%) missing valuesMissing
DBO_TOT has 124 (26.2%) missing valuesMissing
DQO_TOT has 126 (26.6%) missing valuesMissing
COLI_FEC has 21 (4.4%) missing valuesMissing
E_COLI has 124 (26.2%) missing valuesMissing
N_TOT has 53 (11.2%) missing valuesMissing
P_TOT has 57 (12.1%) missing valuesMissing
TOX_D_48_UT has 123 (26.0%) missing valuesMissing
TOX_FIS_SUP_15_UT has 473 (100.0%) missing valuesMissing
TOX_FIS_SUP_15_UT is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-10-05 03:09:56.392509
Analysis finished2024-10-05 03:10:17.931472
Duration21.54 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

CLAVE SITIO
Categorical

HIGH CORRELATION 

Distinct43
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size30.5 KiB
OCNOR3989
42 
OCNOR4026
42 
OCNOR4017
40 
OCNOR4020
40 
OCNOR4019
39 
Other values (38)
270 

Length

Max length35
Median length9
Mean length8.9978858
Min length7

Characters and Unicode

Total characters4256
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)1.7%

Sample

1st rowOCNOR3987
2nd rowOCNOR3987
3rd rowOCNOR3987
4th rowOCNOR3987
5th rowOCNOR3987

Common Values

ValueCountFrequency (%)
OCNOR3989 42
 
8.9%
OCNOR4026 42
 
8.9%
OCNOR4017 40
 
8.5%
OCNOR4020 40
 
8.5%
OCNOR4019 39
 
8.2%
OCNOR3988 36
 
7.6%
OCNOR3990 35
 
7.4%
OCNOR4022 27
 
5.7%
OCNOR4024 25
 
5.3%
OCNOR4023 15
 
3.2%
Other values (33) 132
27.9%

Length

2024-10-04T20:10:18.137204image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ocnor3989 42
 
8.7%
ocnor4026 42
 
8.7%
ocnor4017 40
 
8.3%
ocnor4020 40
 
8.3%
ocnor4019 39
 
8.1%
ocnor3988 36
 
7.5%
ocnor3990 35
 
7.3%
ocnor4022 27
 
5.6%
ocnor4024 25
 
5.2%
ocnor4023 15
 
3.1%
Other values (37) 140
29.1%

Most occurring characters

ValueCountFrequency (%)
O 877
20.6%
C 444
10.4%
N 438
10.3%
R 438
10.3%
0 402
9.4%
4 317
 
7.4%
9 264
 
6.2%
2 236
 
5.5%
3 187
 
4.4%
8 141
 
3.3%
Other values (17) 512
12.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4256
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
O 877
20.6%
C 444
10.4%
N 438
10.3%
R 438
10.3%
0 402
9.4%
4 317
 
7.4%
9 264
 
6.2%
2 236
 
5.5%
3 187
 
4.4%
8 141
 
3.3%
Other values (17) 512
12.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4256
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
O 877
20.6%
C 444
10.4%
N 438
10.3%
R 438
10.3%
0 402
9.4%
4 317
 
7.4%
9 264
 
6.2%
2 236
 
5.5%
3 187
 
4.4%
8 141
 
3.3%
Other values (17) 512
12.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4256
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
O 877
20.6%
C 444
10.4%
N 438
10.3%
R 438
10.3%
0 402
9.4%
4 317
 
7.4%
9 264
 
6.2%
2 236
 
5.5%
3 187
 
4.4%
8 141
 
3.3%
Other values (17) 512
12.0%

ESTADO
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size29.1 KiB
SONORA
473 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters2838
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSONORA
2nd rowSONORA
3rd rowSONORA
4th rowSONORA
5th rowSONORA

Common Values

ValueCountFrequency (%)
SONORA 473
100.0%

Length

2024-10-04T20:10:18.418209image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-04T20:10:18.660685image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
sonora 473
100.0%

Most occurring characters

ValueCountFrequency (%)
O 946
33.3%
S 473
16.7%
N 473
16.7%
R 473
16.7%
A 473
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2838
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
O 946
33.3%
S 473
16.7%
N 473
16.7%
R 473
16.7%
A 473
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2838
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
O 946
33.3%
S 473
16.7%
N 473
16.7%
R 473
16.7%
A 473
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2838
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
O 946
33.3%
S 473
16.7%
N 473
16.7%
R 473
16.7%
A 473
16.7%

MUNICIPIO
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size32.0 KiB
ARIZPE
115 
CANANEA
103 
ACONCHI
82 
BAVIÁCORA
79 
URES
72 
Other values (2)
22 

Length

Max length10
Median length9
Mean length6.7357294
Min length4

Characters and Unicode

Total characters3186
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCANANEA
2nd rowCANANEA
3rd rowCANANEA
4th rowCANANEA
5th rowCANANEA

Common Values

ValueCountFrequency (%)
ARIZPE 115
24.3%
CANANEA 103
21.8%
ACONCHI 82
17.3%
BAVIÁCORA 79
16.7%
URES 72
15.2%
BANÁMICHI 18
 
3.8%
SAN FELIPE 4
 
0.8%

Length

2024-10-04T20:10:18.927006image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-04T20:10:19.230835image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
arizpe 115
24.1%
cananea 103
21.6%
aconchi 82
17.2%
baviácora 79
16.6%
ures 72
15.1%
banámichi 18
 
3.8%
san 4
 
0.8%
felipe 4
 
0.8%

Most occurring characters

ValueCountFrequency (%)
A 686
21.5%
C 364
11.4%
I 316
9.9%
N 310
9.7%
E 298
9.4%
R 266
 
8.3%
O 161
 
5.1%
P 119
 
3.7%
Z 115
 
3.6%
H 100
 
3.1%
Other values (9) 451
14.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3186
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 686
21.5%
C 364
11.4%
I 316
9.9%
N 310
9.7%
E 298
9.4%
R 266
 
8.3%
O 161
 
5.1%
P 119
 
3.7%
Z 115
 
3.6%
H 100
 
3.1%
Other values (9) 451
14.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3186
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 686
21.5%
C 364
11.4%
I 316
9.9%
N 310
9.7%
E 298
9.4%
R 266
 
8.3%
O 161
 
5.1%
P 119
 
3.7%
Z 115
 
3.6%
H 100
 
3.1%
Other values (9) 451
14.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3186
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 686
21.5%
C 364
11.4%
I 316
9.9%
N 310
9.7%
E 298
9.4%
R 266
 
8.3%
O 161
 
5.1%
P 119
 
3.7%
Z 115
 
3.6%
H 100
 
3.1%
Other values (9) 451
14.2%

CUERPO DE AGUA
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size36.2 KiB
RIO SONORA
285 
RIO BACANUCHI
44 
ARROYO EL BARRILITO
42 
ACUÍFERO RIO SONORA
 
28
ACUÍFERO RIO BACANUCHI
 
25
Other values (5)
49 

Length

Max length22
Median length10
Mean length13.145877
Min length10

Characters and Unicode

Total characters6218
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRIO SONORA
2nd rowRIO SONORA
3rd rowRIO SONORA
4th rowRIO SONORA
5th rowRIO SONORA

Common Values

ValueCountFrequency (%)
RIO SONORA 285
60.3%
RIO BACANUCHI 44
 
9.3%
ARROYO EL BARRILITO 42
 
8.9%
ACUÍFERO RIO SONORA 28
 
5.9%
ACUÍFERO RIO BACANUCHI 25
 
5.3%
ACUÍFERO RÍO SONORA 25
 
5.3%
ACUÍFERO CUITACA 9
 
1.9%
ACUÍFERO RIO BACOACHI 9
 
1.9%
ACUÍFERO SAN PEDRO 3
 
0.6%
ACUÍFERO BACANUCHI 3
 
0.6%

Length

2024-10-04T20:10:19.595305image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-04T20:10:19.922240image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
rio 391
36.3%
sonora 338
31.4%
acuífero 102
 
9.5%
bacanuchi 72
 
6.7%
arroyo 42
 
3.9%
el 42
 
3.9%
barrilito 42
 
3.9%
río 25
 
2.3%
cuitaca 9
 
0.8%
bacoachi 9
 
0.8%
Other values (2) 6
 
0.6%

Most occurring characters

ValueCountFrequency (%)
O 1332
21.4%
R 1027
16.5%
A 707
11.4%
605
9.7%
I 565
9.1%
N 413
 
6.6%
S 341
 
5.5%
C 282
 
4.5%
U 183
 
2.9%
E 147
 
2.4%
Other values (9) 616
9.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6218
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
O 1332
21.4%
R 1027
16.5%
A 707
11.4%
605
9.7%
I 565
9.1%
N 413
 
6.6%
S 341
 
5.5%
C 282
 
4.5%
U 183
 
2.9%
E 147
 
2.4%
Other values (9) 616
9.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6218
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
O 1332
21.4%
R 1027
16.5%
A 707
11.4%
605
9.7%
I 565
9.1%
N 413
 
6.6%
S 341
 
5.5%
C 282
 
4.5%
U 183
 
2.9%
E 147
 
2.4%
Other values (9) 616
9.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6218
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
O 1332
21.4%
R 1027
16.5%
A 707
11.4%
605
9.7%
I 565
9.1%
N 413
 
6.6%
S 341
 
5.5%
C 282
 
4.5%
U 183
 
2.9%
E 147
 
2.4%
Other values (9) 616
9.9%

TIPO CUERPO DE AGUA
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size43.0 KiB
LÓTICO
271 
SUBTERRÁNEO
118 
LÓTICO (HUMEDAL)
84 

Length

Max length16
Median length6
Mean length9.0232558
Min length6

Characters and Unicode

Total characters4268
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLÓTICO
2nd rowLÓTICO
3rd rowLÓTICO
4th rowLÓTICO
5th rowLÓTICO

Common Values

ValueCountFrequency (%)
LÓTICO 271
57.3%
SUBTERRÁNEO 118
24.9%
LÓTICO (HUMEDAL) 84
 
17.8%

Length

2024-10-04T20:10:20.317781image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-04T20:10:20.582588image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
lótico 355
63.7%
subterráneo 118
 
21.2%
humedal 84
 
15.1%

Most occurring characters

ValueCountFrequency (%)
T 473
11.1%
O 473
11.1%
L 439
10.3%
Ó 355
 
8.3%
I 355
 
8.3%
C 355
 
8.3%
E 320
 
7.5%
R 236
 
5.5%
U 202
 
4.7%
S 118
 
2.8%
Other values (10) 942
22.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4268
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 473
11.1%
O 473
11.1%
L 439
10.3%
Ó 355
 
8.3%
I 355
 
8.3%
C 355
 
8.3%
E 320
 
7.5%
R 236
 
5.5%
U 202
 
4.7%
S 118
 
2.8%
Other values (10) 942
22.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4268
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 473
11.1%
O 473
11.1%
L 439
10.3%
Ó 355
 
8.3%
I 355
 
8.3%
C 355
 
8.3%
E 320
 
7.5%
R 236
 
5.5%
U 202
 
4.7%
S 118
 
2.8%
Other values (10) 942
22.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4268
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 473
11.1%
O 473
11.1%
L 439
10.3%
Ó 355
 
8.3%
I 355
 
8.3%
C 355
 
8.3%
E 320
 
7.5%
R 236
 
5.5%
U 202
 
4.7%
S 118
 
2.8%
Other values (10) 942
22.1%

SUBTIPO CUERPO AGUA
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size34.4 KiB
RÍO
313 
POZO
115 
ARROYO
42 
TOMA DOMICILIARIA
 
3

Length

Max length17
Median length3
Mean length3.5983087
Min length3

Characters and Unicode

Total characters1702
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRÍO
2nd rowRÍO
3rd rowRÍO
4th rowRÍO
5th rowRÍO

Common Values

ValueCountFrequency (%)
RÍO 313
66.2%
POZO 115
 
24.3%
ARROYO 42
 
8.9%
TOMA DOMICILIARIA 3
 
0.6%

Length

2024-10-04T20:10:20.887176image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-04T20:10:21.164893image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
río 313
65.8%
pozo 115
 
24.2%
arroyo 42
 
8.8%
toma 3
 
0.6%
domiciliaria 3
 
0.6%

Most occurring characters

ValueCountFrequency (%)
O 633
37.2%
R 400
23.5%
Í 313
18.4%
P 115
 
6.8%
Z 115
 
6.8%
A 51
 
3.0%
Y 42
 
2.5%
I 12
 
0.7%
M 6
 
0.4%
T 3
 
0.2%
Other values (4) 12
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1702
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
O 633
37.2%
R 400
23.5%
Í 313
18.4%
P 115
 
6.8%
Z 115
 
6.8%
A 51
 
3.0%
Y 42
 
2.5%
I 12
 
0.7%
M 6
 
0.4%
T 3
 
0.2%
Other values (4) 12
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1702
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
O 633
37.2%
R 400
23.5%
Í 313
18.4%
P 115
 
6.8%
Z 115
 
6.8%
A 51
 
3.0%
Y 42
 
2.5%
I 12
 
0.7%
M 6
 
0.4%
T 3
 
0.2%
Other values (4) 12
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1702
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
O 633
37.2%
R 400
23.5%
Í 313
18.4%
P 115
 
6.8%
Z 115
 
6.8%
A 51
 
3.0%
Y 42
 
2.5%
I 12
 
0.7%
M 6
 
0.4%
T 3
 
0.2%
Other values (4) 12
 
0.7%

LATITUD
Real number (ℝ)

HIGH CORRELATION 

Distinct42
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.112463
Minimum29.32107
Maximum31.08505
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.4 KiB
2024-10-04T20:10:21.668307image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum29.32107
5-th percentile29.32107
Q129.72369
median29.854986
Q330.37335
95-th percentile30.99713
Maximum31.08505
Range1.76398
Interquartile range (IQR)0.64966

Descriptive statistics

Standard deviation0.56808065
Coefficient of variation (CV)0.0188653
Kurtosis-1.1996464
Mean30.112463
Median Absolute Deviation (MAD)0.480044
Skewness0.30824155
Sum14243.195
Variance0.32271563
MonotonicityNot monotonic
2024-10-04T20:10:22.047426image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
30.99713 42
 
8.9%
30.36813 42
 
8.9%
29.72369 40
 
8.5%
29.32107 40
 
8.5%
29.53502 39
 
8.2%
30.9481 36
 
7.6%
29.84478 35
 
7.4%
29.8244 27
 
5.7%
30.33503 25
 
5.3%
30.01704 15
 
3.2%
Other values (32) 132
27.9%
ValueCountFrequency (%)
29.32107 40
8.5%
29.36277 2
 
0.4%
29.37303 3
 
0.6%
29.38781 3
 
0.6%
29.40136 10
 
2.1%
29.42405 1
 
0.2%
29.431096 4
 
0.8%
29.43628 3
 
0.6%
29.43705 3
 
0.6%
29.46299 3
 
0.6%
ValueCountFrequency (%)
31.08505 3
 
0.6%
30.99713 42
8.9%
30.99709 9
 
1.9%
30.975972 3
 
0.6%
30.95804 10
 
2.1%
30.9481 36
7.6%
30.60567 1
 
0.2%
30.603729 3
 
0.6%
30.57567 1
 
0.2%
30.43986 9
 
1.9%

LONGITUD
Real number (ℝ)

HIGH CORRELATION 

Distinct42
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-110.24517
Minimum-110.54156
Maximum-110.03386
Zeros0
Zeros (%)0.0%
Negative473
Negative (%)100.0%
Memory size7.4 KiB
2024-10-04T20:10:22.418665image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-110.54156
5-th percentile-110.54156
Q1-110.28877
median-110.20114
Q3-110.16094
95-th percentile-110.12087
Maximum-110.03386
Range0.5077
Interquartile range (IQR)0.12783

Descriptive statistics

Standard deviation0.12484478
Coefficient of variation (CV)-0.0011324285
Kurtosis0.66689643
Mean-110.24517
Median Absolute Deviation (MAD)0.04363
Skewness-1.2264553
Sum-52145.965
Variance0.015586218
MonotonicityNot monotonic
2024-10-04T20:10:22.783793image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
-110.28877 42
 
8.9%
-110.15751 42
 
8.9%
-110.17441 40
 
8.5%
-110.54156 40
 
8.5%
-110.12087 39
 
8.2%
-110.19079 36
 
7.6%
-110.27754 35
 
7.4%
-110.23701 27
 
5.7%
-110.16534 25
 
5.3%
-110.21963 15
 
3.2%
Other values (32) 132
27.9%
ValueCountFrequency (%)
-110.54156 40
8.5%
-110.50018 2
 
0.4%
-110.48888 9
 
1.9%
-110.46914 3
 
0.6%
-110.45391 3
 
0.6%
-110.43803 10
 
2.1%
-110.41217 3
 
0.6%
-110.391199 4
 
0.8%
-110.38294 1
 
0.2%
-110.36998 3
 
0.6%
ValueCountFrequency (%)
-110.03386 9
 
1.9%
-110.05843 1
 
0.2%
-110.11187 3
 
0.6%
-110.12087 39
8.2%
-110.15732 3
 
0.6%
-110.15751 42
8.9%
-110.15789 12
 
2.5%
-110.16094 12
 
2.5%
-110.16534 25
5.3%
-110.17441 40
8.5%

OD_mg/L
Real number (ℝ)

MISSING 

Distinct263
Distinct (%)73.3%
Missing114
Missing (%)24.1%
Infinite0
Infinite (%)0.0%
Mean6.6690251
Minimum1
Maximum13.81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.4 KiB
2024-10-04T20:10:23.113685image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.736
Q15.6
median6.66
Q37.635
95-th percentile9.702
Maximum13.81
Range12.81
Interquartile range (IQR)2.035

Descriptive statistics

Standard deviation1.81226
Coefficient of variation (CV)0.27174288
Kurtosis0.80368121
Mean6.6690251
Median Absolute Deviation (MAD)1.04
Skewness0.096850657
Sum2394.18
Variance3.2842865
MonotonicityNot monotonic
2024-10-04T20:10:23.330797image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.19 6
 
1.3%
7.2 6
 
1.3%
6.1 5
 
1.1%
5.8 4
 
0.8%
7.21 3
 
0.6%
5.7 3
 
0.6%
6.7 3
 
0.6%
7.56 3
 
0.6%
7.1 3
 
0.6%
6.13 3
 
0.6%
Other values (253) 320
67.7%
(Missing) 114
 
24.1%
ValueCountFrequency (%)
1 1
0.2%
1.03 1
0.2%
2.07 1
0.2%
2.47 1
0.2%
2.48 1
0.2%
2.61 1
0.2%
2.62 1
0.2%
2.69 1
0.2%
2.8 1
0.2%
2.82 1
0.2%
ValueCountFrequency (%)
13.81 1
0.2%
11.89 1
0.2%
11.59 1
0.2%
10.85 1
0.2%
10.69 1
0.2%
10.66 1
0.2%
10.65 1
0.2%
10.6 1
0.2%
10.56 1
0.2%
10.52 1
0.2%

DBO_TOT
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct139
Distinct (%)39.8%
Missing124
Missing (%)26.2%
Infinite0
Infinite (%)0.0%
Mean6.8139255
Minimum2
Maximum256
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.4 KiB
2024-10-04T20:10:23.694877image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q12
median2
Q34.79
95-th percentile20.94
Maximum256
Range254
Interquartile range (IQR)2.79

Descriptive statistics

Standard deviation18.805719
Coefficient of variation (CV)2.7598951
Kurtosis102.5782
Mean6.8139255
Median Absolute Deviation (MAD)0
Skewness9.097508
Sum2378.06
Variance353.65508
MonotonicityNot monotonic
2024-10-04T20:10:24.089870image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 200
42.3%
2.8 3
 
0.6%
3.41 2
 
0.4%
6.7 2
 
0.4%
3.6 2
 
0.4%
2.4 2
 
0.4%
5.8 2
 
0.4%
6.1 2
 
0.4%
9.5 2
 
0.4%
4.3 2
 
0.4%
Other values (129) 130
27.5%
(Missing) 124
26.2%
ValueCountFrequency (%)
2 200
42.3%
2.08 1
 
0.2%
2.09 1
 
0.2%
2.4 2
 
0.4%
2.42 1
 
0.2%
2.58 1
 
0.2%
2.6 1
 
0.2%
2.61 1
 
0.2%
2.72 1
 
0.2%
2.77 1
 
0.2%
ValueCountFrequency (%)
256 1
0.2%
155.98 1
0.2%
104.51 1
0.2%
84.46 1
0.2%
70.6 1
0.2%
62.5 1
0.2%
60.9 1
0.2%
45.25 1
0.2%
38.4 1
0.2%
30.81 1
0.2%

DQO_TOT
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct183
Distinct (%)52.7%
Missing126
Missing (%)26.6%
Infinite0
Infinite (%)0.0%
Mean46.665715
Minimum10
Maximum1600
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.4 KiB
2024-10-04T20:10:24.452501image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile10
Q110
median12.96
Q342.46
95-th percentile147.936
Maximum1600
Range1590
Interquartile range (IQR)32.46

Descriptive statistics

Standard deviation121.54669
Coefficient of variation (CV)2.6046251
Kurtosis92.35973
Mean46.665715
Median Absolute Deviation (MAD)2.96
Skewness8.6006909
Sum16193.003
Variance14773.599
MonotonicityNot monotonic
2024-10-04T20:10:24.853678image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 159
33.6%
13.38 3
 
0.6%
31.31 2
 
0.4%
21.1 2
 
0.4%
71.91 2
 
0.4%
11.02 2
 
0.4%
136.68 1
 
0.2%
65 1
 
0.2%
12.99 1
 
0.2%
53.3544 1
 
0.2%
Other values (173) 173
36.6%
(Missing) 126
26.6%
ValueCountFrequency (%)
10 159
33.6%
10.92 1
 
0.2%
10.97 1
 
0.2%
11 1
 
0.2%
11.02 2
 
0.4%
11.23 1
 
0.2%
11.76 1
 
0.2%
11.8 1
 
0.2%
11.87 1
 
0.2%
11.93 1
 
0.2%
ValueCountFrequency (%)
1600 1
0.2%
1020.572 1
0.2%
712.08 1
0.2%
637.56 1
0.2%
380 1
0.2%
364.32 1
0.2%
344.74 1
0.2%
281.52 1
0.2%
240 1
0.2%
233.79 1
0.2%

COLI_FEC
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct181
Distinct (%)40.0%
Missing21
Missing (%)4.4%
Infinite0
Infinite (%)0.0%
Mean18321.332
Minimum1
Maximum2419600
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.4 KiB
2024-10-04T20:10:25.186745image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q110
median870.5
Q35172
95-th percentile25705.3
Maximum2419600
Range2419599
Interquartile range (IQR)5162

Descriptive statistics

Standard deviation121228.21
Coefficient of variation (CV)6.6167794
Kurtosis343.26197
Mean18321.332
Median Absolute Deviation (MAD)860.5
Skewness17.511513
Sum8281242
Variance1.4696279 × 1010
MonotonicityNot monotonic
2024-10-04T20:10:25.580671image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 71
 
15.0%
24196 45
 
9.5%
1 43
 
9.1%
241960 14
 
3.0%
20 9
 
1.9%
3 6
 
1.3%
31 5
 
1.1%
110 5
 
1.1%
2046 4
 
0.8%
41 4
 
0.8%
Other values (171) 246
52.0%
(Missing) 21
 
4.4%
ValueCountFrequency (%)
1 43
9.1%
3 6
 
1.3%
10 71
15.0%
20 9
 
1.9%
31 5
 
1.1%
41 4
 
0.8%
63 2
 
0.4%
74 1
 
0.2%
75 2
 
0.4%
86 2
 
0.4%
ValueCountFrequency (%)
2419600 1
 
0.2%
241960 14
 
3.0%
198630 1
 
0.2%
120330 1
 
0.2%
81640 2
 
0.4%
48840 1
 
0.2%
35240 1
 
0.2%
30760 1
 
0.2%
27550 1
 
0.2%
24196 45
9.5%

E_COLI
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct143
Distinct (%)41.0%
Missing124
Missing (%)26.2%
Infinite0
Infinite (%)0.0%
Mean12458.756
Minimum1
Maximum2419600
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.4 KiB
2024-10-04T20:10:25.959261image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q120
median110
Q3630
95-th percentile24196
Maximum2419600
Range2419599
Interquartile range (IQR)610

Descriptive statistics

Standard deviation131109.28
Coefficient of variation (CV)10.523465
Kurtosis329.21804
Mean12458.756
Median Absolute Deviation (MAD)107
Skewness17.920857
Sum4348106
Variance1.7189644 × 1010
MonotonicityNot monotonic
2024-10-04T20:10:26.349280image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 42
 
8.9%
10 37
 
7.8%
24196 26
 
5.5%
20 15
 
3.2%
52 14
 
3.0%
41 12
 
2.5%
31 9
 
1.9%
63 7
 
1.5%
110 5
 
1.1%
3 5
 
1.1%
Other values (133) 177
37.4%
(Missing) 124
26.2%
ValueCountFrequency (%)
1 42
8.9%
3 5
 
1.1%
10 37
7.8%
20 15
 
3.2%
30 4
 
0.8%
31 9
 
1.9%
41 12
 
2.5%
52 14
 
3.0%
62 1
 
0.2%
63 7
 
1.5%
ValueCountFrequency (%)
2419600 1
 
0.2%
241960 2
 
0.4%
129970 1
 
0.2%
104620 1
 
0.2%
98040 1
 
0.2%
64880 1
 
0.2%
51720 1
 
0.2%
30760 1
 
0.2%
27550 1
 
0.2%
24196 26
5.5%

N_TOT
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct420
Distinct (%)100.0%
Missing53
Missing (%)11.2%
Infinite0
Infinite (%)0.0%
Mean3.6501564
Minimum0.0115
Maximum68.779
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.4 KiB
2024-10-04T20:10:26.711241image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.0115
5-th percentile0.2297829
Q11.076209
median2.055677
Q33.6715368
95-th percentile12.728948
Maximum68.779
Range68.7675
Interquartile range (IQR)2.5953278

Descriptive statistics

Standard deviation5.7822411
Coefficient of variation (CV)1.5841078
Kurtosis48.416697
Mean3.6501564
Median Absolute Deviation (MAD)1.109727
Skewness5.7149068
Sum1533.0657
Variance33.434312
MonotonicityNot monotonic
2024-10-04T20:10:27.016620image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.923684 1
 
0.2%
2.346321 1
 
0.2%
2.41455 1
 
0.2%
2.53051 1
 
0.2%
3.23003 1
 
0.2%
2.53015 1
 
0.2%
3.04269 1
 
0.2%
2.625585 1
 
0.2%
2.406978 1
 
0.2%
1.068825 1
 
0.2%
Other values (410) 410
86.7%
(Missing) 53
 
11.2%
ValueCountFrequency (%)
0.0115 1
0.2%
0.041416 1
0.2%
0.058 1
0.2%
0.073145 1
0.2%
0.07383 1
0.2%
0.086157 1
0.2%
0.089425 1
0.2%
0.124953 1
0.2%
0.127888 1
0.2%
0.153916 1
0.2%
ValueCountFrequency (%)
68.779 1
0.2%
45.998 1
0.2%
36.511485 1
0.2%
29.643392 1
0.2%
25.383 1
0.2%
23.03146 1
0.2%
22.699553 1
0.2%
19.139493 1
0.2%
18.796267 1
0.2%
18.22653 1
0.2%

P_TOT
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct393
Distinct (%)94.5%
Missing57
Missing (%)12.1%
Infinite0
Infinite (%)0.0%
Mean0.41443252
Minimum0.001
Maximum18.841
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.4 KiB
2024-10-04T20:10:27.281817image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile0.017875
Q10.038995
median0.0622425
Q30.13073
95-th percentile2.137312
Maximum18.841
Range18.84
Interquartile range (IQR)0.091735

Descriptive statistics

Standard deviation1.2844076
Coefficient of variation (CV)3.0991959
Kurtosis107.87192
Mean0.41443252
Median Absolute Deviation (MAD)0.0302425
Skewness8.7255405
Sum172.40393
Variance1.6497028
MonotonicityNot monotonic
2024-10-04T20:10:27.676970image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.044 4
 
0.8%
0.0014 3
 
0.6%
0.062 3
 
0.6%
0.04 3
 
0.6%
0.037 3
 
0.6%
0.0719 2
 
0.4%
0.032 2
 
0.4%
0.0558 2
 
0.4%
0.034 2
 
0.4%
0.035 2
 
0.4%
Other values (383) 390
82.5%
(Missing) 57
 
12.1%
ValueCountFrequency (%)
0.001 1
 
0.2%
0.0014 3
0.6%
0.005 1
 
0.2%
0.007832 1
 
0.2%
0.008 1
 
0.2%
0.009135 1
 
0.2%
0.0095 1
 
0.2%
0.0096 1
 
0.2%
0.010779 1
 
0.2%
0.01111 1
 
0.2%
ValueCountFrequency (%)
18.841 1
0.2%
8.62 1
0.2%
6.524994 1
0.2%
5.59863 1
0.2%
5.0175 1
0.2%
4.127732 1
0.2%
3.9559 1
0.2%
3.7813 1
0.2%
3.53704 1
0.2%
3.147842 1
0.2%

TOX_D_48_UT
Real number (ℝ)

MISSING 

Distinct32
Distinct (%)9.1%
Missing123
Missing (%)26.0%
Infinite0
Infinite (%)0.0%
Mean27.5366
Minimum1
Maximum1250
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.4 KiB
2024-10-04T20:10:28.253415image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile118.537
Maximum1250
Range1249
Interquartile range (IQR)0

Descriptive statistics

Standard deviation131.71713
Coefficient of variation (CV)4.7833477
Kurtosis46.669703
Mean27.5366
Median Absolute Deviation (MAD)0
Skewness6.4995157
Sum9637.81
Variance17349.403
MonotonicityNot monotonic
2024-10-04T20:10:28.600646image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
1 317
67.0%
714.29 2
 
0.4%
400 2
 
0.4%
1.46 1
 
0.2%
322.58 1
 
0.2%
1000 1
 
0.2%
1250 1
 
0.2%
149.25 1
 
0.2%
208.33 1
 
0.2%
666.67 1
 
0.2%
Other values (22) 22
 
4.7%
(Missing) 123
 
26.0%
ValueCountFrequency (%)
1 317
67.0%
1.28 1
 
0.2%
1.3 1
 
0.2%
1.46 1
 
0.2%
1.59 1
 
0.2%
2.51 1
 
0.2%
2.97 1
 
0.2%
7.37 1
 
0.2%
20.63 1
 
0.2%
49.08 1
 
0.2%
ValueCountFrequency (%)
1250 1
0.2%
1092.42 1
0.2%
1000 1
0.2%
714.29 2
0.4%
666.67 1
0.2%
447.83 1
0.2%
403.18 1
0.2%
400 2
0.4%
322.58 1
0.2%
309.26 1
0.2%

TOX_FIS_SUP_15_UT
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing473
Missing (%)100.0%
Memory size7.4 KiB

Interactions

2024-10-04T20:10:14.175213image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:09:57.348990image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:09:59.027226image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:10:00.693256image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:10:02.453160image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:10:04.937709image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:10:06.750652image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:10:08.546422image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:10:10.336402image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:10:11.874489image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:10:14.435048image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:09:57.528477image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:09:59.221573image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:10:00.864722image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:10:02.632469image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:10:05.146741image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:10:06.900720image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:10:08.737601image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:10:10.485956image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:10:12.056667image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:10:14.675915image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:09:57.696534image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:09:59.358253image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:10:01.030363image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:10:02.789640image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:10:05.290774image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:10:07.052320image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:10:08.915464image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:10:10.618115image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:10:12.201858image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:10:14.919935image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:09:57.847366image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:09:59.522319image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:10:01.193100image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:10:02.959415image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:10:05.467795image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:10:07.272198image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:10:09.090599image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:10:10.759330image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:10:12.464296image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:10:15.189039image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:09:57.996880image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:09:59.674572image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:10:01.372869image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:10:03.127615image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:10:05.657909image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:10:07.478014image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:10:09.256986image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:10:10.887882image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:10:12.707866image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:10:15.458063image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:09:58.181624image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:09:59.852096image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:10:01.551745image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:10:03.336126image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:10:05.864581image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:10:07.676610image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:10:09.429808image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:10:11.042512image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:10:12.948945image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:10:15.702700image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:09:58.355974image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:10:00.022400image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:10:01.720923image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:10:03.490037image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:10:06.046169image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:10:07.856633image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:10:09.608329image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:10:11.173573image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:10:13.193523image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:10:15.942201image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:09:58.501486image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:10:00.176760image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:10:01.889230image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:10:03.658978image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:10:06.234697image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:10:07.990291image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:10:09.780521image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:10:11.306408image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:10:13.430449image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:10:16.206093image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:09:58.688506image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:10:00.351357image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:10:02.054001image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:10:04.612413image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:10:06.385313image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:10:08.178934image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:10:09.976626image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:10:11.451671image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:10:13.695361image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:10:16.457608image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:09:58.869620image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:10:00.528478image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:10:02.266204image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:10:04.751383image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:10:06.566814image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:10:08.369792image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:10:10.167371image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:10:11.593594image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-04T20:10:13.968317image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-10-04T20:10:28.880133image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
CLAVE SITIOCOLI_FECCUERPO DE AGUADBO_TOTDQO_TOTE_COLILATITUDLONGITUDMUNICIPION_TOTOD_mg/LP_TOTSUBTIPO CUERPO AGUATIPO CUERPO DE AGUATOX_D_48_UT
CLAVE SITIO1.0000.0000.9640.0000.2080.0000.9630.9640.9610.2190.0000.0000.9580.9570.161
COLI_FEC0.0001.0000.0480.2920.4350.852-0.068-0.0050.0000.377-0.1910.4800.1250.076-0.432
CUERPO DE AGUA0.9640.0481.0000.2280.0000.1260.5750.6130.4380.2890.0620.0190.7770.7870.000
DBO_TOT0.0000.2920.2281.0000.5840.4050.298-0.1660.0000.262-0.0300.4010.3450.219-0.028
DQO_TOT0.2080.4350.0000.5841.0000.5150.218-0.1220.0000.340-0.0720.5010.0000.000-0.038
E_COLI0.0000.8520.1260.4050.5151.0000.081-0.0070.0000.576-0.2460.6050.0340.000-0.393
LATITUD0.963-0.0680.5750.2980.2180.0811.0000.1800.8450.040-0.2310.2350.4210.4460.336
LONGITUD0.964-0.0050.613-0.166-0.122-0.0070.1801.0000.679-0.0380.131-0.0690.6710.677-0.023
MUNICIPIO0.9610.0000.4380.0000.0000.0000.8450.6791.0000.1760.1180.0000.3930.4800.105
N_TOT0.2190.3770.2890.2620.3400.5760.040-0.0380.1761.000-0.2300.5050.5630.375-0.189
OD_mg/L0.000-0.1910.062-0.030-0.072-0.246-0.2310.1310.118-0.2301.000-0.2810.2100.1540.110
P_TOT0.0000.4800.0190.4010.5010.6050.235-0.0690.0000.505-0.2811.0000.1680.134-0.185
SUBTIPO CUERPO AGUA0.9580.1250.7770.3450.0000.0340.4210.6710.3930.5630.2100.1681.0000.8440.000
TIPO CUERPO DE AGUA0.9570.0760.7870.2190.0000.0000.4460.6770.4800.3750.1540.1340.8441.0000.000
TOX_D_48_UT0.161-0.4320.000-0.028-0.038-0.3930.336-0.0230.105-0.1890.110-0.1850.0000.0001.000

Missing values

2024-10-04T20:10:16.764194image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-04T20:10:17.323074image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-10-04T20:10:17.657661image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

CLAVE SITIOESTADOMUNICIPIOCUERPO DE AGUATIPO CUERPO DE AGUASUBTIPO CUERPO AGUALATITUDLONGITUDOD_mg/LDBO_TOTDQO_TOTCOLI_FECE_COLIN_TOTP_TOTTOX_D_48_UTTOX_FIS_SUP_15_UT
94719OCNOR3987SONORACANANEARIO SONORALÓTICORÍO30.95804-110.189067.193.4117.5392145.020.02.29160.06171.0NaN
94720OCNOR3987SONORACANANEARIO SONORALÓTICORÍO30.95804-110.189068.013.0510.00003.03.00.74980.09311.0NaN
94721OCNOR3987SONORACANANEARIO SONORALÓTICORÍO30.95804-110.189069.246.7051.0840110.010.00.76430.10291.0NaN
94722OCNOR3987SONORACANANEARIO SONORALÓTICORÍO30.95804-110.189067.072.9710.0000110.031.00.92690.11271.0NaN
94723OCNOR3987SONORACANANEARIO SONORALÓTICORÍO30.95804-110.189066.332.8322.73603448.0110.01.32380.13311.0NaN
94724OCNOR3987SONORACANANEARIO SONORALÓTICORÍO30.95804-110.189066.182.80136.680011199.0203.01.16160.17311.0NaN
94725OCNOR3987SONORACANANEARIO SONORALÓTICORÍO30.95804-110.189065.893.3331.20001860.020.00.77590.05991.0NaN
94726OCNOR3987SONORACANANEARIO SONORALÓTICORÍO30.95804-110.189067.275.3312.9900759.0146.00.92780.13701.0NaN
94727OCNOR3987SONORACANANEARIO SONORALÓTICORÍO30.95804-110.189064.807.7065.0000241960.0241960.03.73700.66301.0NaN
94728OCNOR3987SONORACANANEARIO SONORALÓTICORÍO30.95804-110.189066.102.0017.78001664.0146.01.39100.05601.0NaN
CLAVE SITIOESTADOMUNICIPIOCUERPO DE AGUATIPO CUERPO DE AGUASUBTIPO CUERPO AGUALATITUDLONGITUDOD_mg/LDBO_TOTDQO_TOTCOLI_FECE_COLIN_TOTP_TOTTOX_D_48_UTTOX_FIS_SUP_15_UT
125889MET-012SONORAARIZPEACUÍFERO RIO SONORASUBTERRÁNEOPOZO30.29633-110.18674NaNNaNNaN10.0NaNNaNNaNNaNNaN
125890MET-012SONORAARIZPEACUÍFERO RIO SONORASUBTERRÁNEOPOZO30.29633-110.18674NaNNaNNaNNaNNaNNaNNaNNaNNaN
125891MET-003SONORAARIZPERIO BACANUCHILÓTICORÍO30.57567-110.220886.022.010.001658.01274.0NaNNaN1.0NaN
125898MET-051SONORAACONCHIACUÍFERO RIO SONORASUBTERRÁNEOPOZO29.79459-110.21194NaNNaNNaN10.0NaNNaNNaNNaNNaN
125899MET-051SONORAACONCHIACUÍFERO RIO SONORASUBTERRÁNEOPOZO29.79459-110.21194NaNNaNNaNNaNNaNNaNNaNNaNNaN
125905MET-045SONORAURESRIO SONORALÓTICORÍO29.36277-110.500186.5815.324.13120330.022470.0NaNNaN1.0NaN
125906MET-045SONORAURESRIO SONORALÓTICORÍO29.36277-110.50018NaNNaNNaNNaNNaNNaNNaNNaNNaN
126159TOMA DOMICILIARIA BACANUCHI ESCUELASONORAARIZPEACUÍFERO RIO BACANUCHISUBTERRÁNEOTOMA DOMICILIARIA30.60567-110.23583NaNNaNNaN10.0NaNNaNNaNNaNNaN
126164TOMA DOMICILIARIA ACONCHISONORAACONCHIACUÍFERO RIO SONORASUBTERRÁNEOTOMA DOMICILIARIA29.82151-110.22702NaNNaNNaN10.0NaNNaNNaNNaNNaN
126167TOMA DOMICILIARIA LA ESTANCIASONORAACONCHIACUÍFERO RIO SONORASUBTERRÁNEOTOMA DOMICILIARIA29.79699-110.21546NaNNaNNaN148.0NaNNaNNaNNaNNaN